A denoising diffusion model trained only on synthetic brain phantoms, with explicit physics-based data consistency, produces high-accuracy quantitative T1/T2/PD maps from fourfold-accelerated MuPa-ZTE acquisitions and generalizes to real scans.
Conditional image synthesis with diffusion models: A survey,
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HITL-D combines diffusion policies with human input for shared robotic control, reducing required joystick axes and improving speed and workload in manipulation tasks per a 12-participant study.
Latent Wavelet Diffusion uses wavelet energy map masking and a scale-consistent VAE to improve detail fidelity in 2K-4K image generation without extra inference overhead.
A score-based method is introduced to guide optimization in geometric view diffusion models toward correct viewpoints, improving convergence and sample efficiency over naive multistart strategies.
citing papers explorer
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q3-MuPa: Quick, Quiet, Quantitative Multi-Parametric MRI using Physics-Informed Diffusion Models
A denoising diffusion model trained only on synthetic brain phantoms, with explicit physics-based data consistency, produces high-accuracy quantitative T1/T2/PD maps from fourfold-accelerated MuPa-ZTE acquisitions and generalizes to real scans.
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HITL-D: Human In The Loop Diffusion Assisted Shared Control
HITL-D combines diffusion policies with human input for shared robotic control, reducing required joystick axes and improving speed and workload in manipulation tasks per a 12-participant study.
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Latent Wavelet Diffusion For Ultra-High-Resolution Image Synthesis
Latent Wavelet Diffusion uses wavelet energy map masking and a scale-consistent VAE to improve detail fidelity in 2K-4K image generation without extra inference overhead.
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Landscape-Awareness for Geometric View Diffusion Model
A score-based method is introduced to guide optimization in geometric view diffusion models toward correct viewpoints, improving convergence and sample efficiency over naive multistart strategies.